1. Field of the Invention
The present invention relates to a method and an apparatus for checking the input-output characteristic of a neural network.
2. Discussion of the Prior Art
A neural network has been known as a network which memorizes a sequence of cause and effect. Such neural network is composed of an input layer, an intermediate layer and an output layer, and each of layers having plural nodes. Each node of the input layer is related to the respective nodes of the intermediate layer with respective connection weights or connection strengths, while each node of the intermediate layer is related to the respective nodes of the output layer with respective connection weights. These connection weights are corrected through learning operation to optimize the neural network.
The above-mentioned neural network is recently used in machine tools for calculating optimum machining data based upon input and fixed condition data so as to properly carry out required machining operations.
During learning operation, dispersed plural input data are given to the neural network so as to correct or modify the connection weights so that reasonable output data are always obtained.
Namely, one pair of input data and teaching data are firstly used to correct the connection weights so that the output data from the neural network approaches the teaching data, which represent optimum values corresponding to the input data. This learning operation is repeated using a different pair of input and teaching data, and the teaching operation is stopped when all the pairs of input and teaching data are used for the learning operation. By repeating the learning operation, the connection weights are corrected and finally approach specific values so that output data equal to the teaching data are output when corresponding input data are given to the neural network. After the learning operation, the neural network exhibits a desirable input-output characteristic.
However, even after the above-mentioned learning operation, the neural network sometimes outputs inappropriate or abnormal output data in course of actual use in which the neural network is applied to many input data.
In such case, a cause of the abnormality is analyzed using specific input data which induce the abnormal output.
However, it is very difficult to analyze the cause of the abnormality by analyzing the input data, because the neural network is a black box for an operator. Further, even if the operator checks the connection weights of the neural network, it is extremely difficult for the operator to grasp how the connection weights affect the output data from the neural network.
Accordingly, in the conventional neural network system, the various input data are given to the neural network to obtain various output data, and allowable variation ranges of the input data are determined which can be input to the neural network, or which do not induce abnormal output data. However, it is extremely difficult to grasp the characteristics of the neural network through the above-mentioned work, because the neural network is a network in which the components of the input data given to the plural input nodes of the neural network mutually affect each other in course of calculation of the output data.